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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Tool Design & MCP Integration | 18% | - Model Context Protocol (MCP) architecture and JSON-RPC 2.0 - Tool schema design and interface boundaries - Tool distribution and permission controls - MCP tool, resource and prompt implementation - Error handling and tool response formatting |
| Topic 2: Claude Code Configuration & Workflows | 20% | - CLAUDE.md hierarchy, precedence and @import rules - Custom slash commands and plan mode vs direct execution - Hooks vs advisory instructions - Path-specific rules and .claude/rules/ configuration - CI/CD integration and non-interactive mode parameters |
| Topic 3: Prompt Engineering & Structured Output | 20% | - System prompt design and persona alignment - JSON schema design and structured output enforcement - Validation, parsing and retry loop strategies - Explicit criteria definition and few-shot prompting |
| Topic 4: Agentic Architecture & Orchestration | 27% | - Error recovery, guardrails and safety patterns - Agentic loop design and stop_reason handling - Multi-agent patterns: coordinator-subagent and hub-and-spoke - Session state management and workflow enforcement - Task decomposition and dynamic subagent selection |
| Topic 5: Context Management & Reliability | 15% | - Context pruning and summarization strategies - Token budget management and cost control - Idempotency, consistency and failure resilience - Context window optimization and prioritization |
>> Real Anthropic CCAR-F Questions <<
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NEW QUESTION # 161
You are building a structured data-extraction system using Claude. The system extracts information from unstructured documents, validates output against JSON schemas, and integrates the results with downstream systems.
Monitoring reveals that specifications sometimes appear inconsistently within source documents.
For example, a summary section might state "Battery: 4000 mAh," while the detailed specifications table states "Battery: 4200 mAh." Your current schema contains a single battery_capacity field.
This inconsistency occurs in approximately 15% of documents, and historical analysis confirms that the detailed specifications table is accurate 90% of the time.
What is the most effective approach?
Answer: B
Explanation:
Option C converts an empirically validated source hierarchy into an explicit extraction rule. The downstream contract requires one battery_capacity value, and historical analysis establishes that the detailed specifications table is substantially more reliable than the summary section. Claude should therefore be instructed to inspect all occurrences, detect conflicts, and select the detailed- table value when the two locations disagree.
Anthropic's prompting guidance emphasizes clear, direct instructions, relevant context, and explicit decision rules when order or completeness matters. Providing the reason for the precedence rule also helps the model generalize it to comparable specification conflicts.
NEW QUESTION # 162
You are building a customer support resolution agent using the Claude Agent SDK. The agent handles high- ambiguity requests like returns, billing disputes, and account issues. It has access to your backend systems through custom Model Context Protocol (MCP) tools ( get_customer , lookup_order , process_refund , escalate_to_human ). Your target is 80%+ first-contact resolution while knowing when to escalate.
When the agent calls lookup_order and receives order details showing the item was purchased 45 days ago, how does the agentic loop determine whether to call process_refund or escalate_to_human next?
Answer: C
Explanation:
In the standard Claude tool-use loop, the application executes lookup_order and sends its output back as a tool_result . That result becomes part of the conversation state available to Claude. Claude then evaluates the purchase date, refund policy, customer request, authorization constraints, and available tools before selecting the next action.
Anthropic describes client-tool orchestration as a repeated loop: Claude emits a tool_use request, the application executes it, returns a tool_result , and Claude continues reasoning from the updated conversation.
Claude, rather than the tool implementation, selects when and how to invoke the next available tool unless the application has explicitly implemented a fixed workflow. ( https://platform.claude.com/docs/en/agents-and- tools/tool-use/how-tool-use-works ) Options B and C describe possible custom orchestration architectures, but neither is stated in the scenario.
Option D is inconsistent with adaptive agent behavior because later actions depend on information that did not exist before lookup_order completed. A rigid sequence would not respond appropriately to different purchase dates, eligibility states, or order conditions.
The tool result should return high-signal fields such as purchase date, return-window status, refund eligibility, existing refund status, and stable order identifiers so Claude can make the subsequent decision accurately.
Official references/topics: Tool-result continuation, adaptive agent loops, model-directed tool selection, sequential dependency handling.
NEW QUESTION # 163
The synthesis agent completes its initial pass but flags that three key research questions remain unanswered because the web search and document analysis agents didn't find relevant information on those specific subtopics. The coordinator currently proceeds directly to report generation, producing reports with incomplete coverage. What change would most effectively improve research completeness?
Answer: A
Explanation:
The coordinator should treat unanswered research questions as gaps in the workflow and route them back to the appropriate specialist agents with targeted follow-up tasks. This creates an iterative research loop that improves completeness before the final report is generated.
NEW QUESTION # 164
You are using Claude Code to accelerate software development. Your team uses it for code generation, refactoring, debugging, and documentation. You need to integrate it into your development workflow with custom slash commands, CLAUDE.md configurations, and understand when to use plan mode vs direct execution.
Your infrastructure-as-code repository includes Terraform modules ( /terraform/ ), Kubernetes manifests (
/kubernetes/ ), and CI/CD pipeline scripts ( /pipelines/ ). Each requires different conventions, but your single root CLAUDE.md has grown to 500+ lines. When developers work on Kubernetes files, Terraform-specific rules load into context unnecessarily, consuming tokens.
What is the best approach to reorganize so only relevant guidance loads when editing specific file types?
Answer: B
Explanation:
Path-scoped rules directly satisfy the requirement that instructions load only when Claude works with matching files. Each infrastructure domain can have a separate Markdown file under .claude/rules/ , with YAML paths frontmatter targeting Terraform files, Kubernetes manifests, or pipeline configuration.
Anthropic documents that .claude/rules/ keeps project guidance modular and supports conditional loading based on glob patterns. Rules with a paths field apply only when Claude works with matching files, reducing irrelevant context and token consumption. Rules without path frontmatter load unconditionally. ( https://code.
claude.com/docs/en/memory )
Option B improves readability but leaves all 500-plus lines in every session. Option C can provide directory- specific CLAUDE.md guidance, but path-scoped rules are more precise when conventions depend on extensions or patterns spanning multiple directories. Option D still requires manual imports and does not provide the documented conditional rule-loading mechanism.
A suitable design would use rules such as terraform.md scoped to terraform/**/*.tf , kubernetes.md scoped to kubernetes/**/*.{yaml,yml} , and pipelines.md scoped to the pipeline directory. Broad project-wide commands and repository etiquette can remain in the root CLAUDE.md.
Official references/topics: .claude/rules/ ; Path-Specific Rules; YAML Frontmatter; Context-Efficient Instruction Loading.
NEW QUESTION # 165
Your agent is handling a billing dispute. After calling get_customer and lookup_order, it identifies that the dispute involves a promotional pricing error requiring manager approval - beyond the agent's authorization level. How should the workflow handle this mid-process escalation?
Answer: C
Explanation:
A structured handoff containing the customer details, order information, and the specific issue ensures the human agent has all relevant context to act immediately. This approach avoids delays or repeated clarification and preserves continuity when authority limits prevent the agent from completing the task.
NEW QUESTION # 166
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